import torch
import torch.nn as nn
import torch.nn.functional as F




class VeryBasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(VeryBasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride,padding=1,  bias=False)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False)
            )

    def forward(self, x):
        out = F.relu(self.conv1(x))
        out = self.conv2(out)
        out += self.shortcut(x)
        out = F.relu(out)
        return out




class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion*planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear = nn.Linear(512*block.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def _make_block(self,in_planes, planes, stride=1):
        return nn.Sequential(nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False),
            nn.ReLU(inplace=True),
            nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False),
            nn.ReLU(inplace=True)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))        
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out



class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10, bad_conditioning = False ):
        super(ResNet, self).__init__()
        self.in_planes = 64
        self.bad_conditioning = bad_conditioning
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding = 1, bias=False)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear = nn.Linear(512*block.expansion, num_classes)
        self.pad_1 = (1,1,1,1,0,0,0,0)
        if self.bad_conditioning:
            self.non_linearity  = torch.nn.Tanhshrink()

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def _make_block(self,in_planes, planes, stride=1):
        return nn.Sequential(nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, bias=False),
            nn.ReLU(inplace=True),
            nn.Conv2d(planes, planes, kernel_size=3, stride=1, bias=False),
            nn.ReLU(inplace=True)
            )

    def forward(self, x):
        out = F.relu(self.conv1(x))

        out = self.layer1(out)
 
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)

        out = self.linear(out)
        
        if self.bad_conditioning:
            cond_weights = torch.logspace(start=-6,end=1,steps=out.shape[1],dtype=out.dtype, device=out.device)
            out = torch.einsum('bm,m->bm',out,cond_weights)


        return out


def ResNetBasic(num_classes=10):
    return ResNet(VeryBasicBlock, [2,2,2,2],num_classes=num_classes)

def ResNet18(num_classes=10):
    return ResNet(BasicBlock, [2,2,2,2],num_classes=num_classes)

def ResNet18IllCond(num_classes=10):
    return ResNet(BasicBlock, [2,2,2,2], bad_conditioning = True,num_classes=num_classes)

def ResNetBasicIllCond(num_classes=10):
    return ResNet(VeryBasicBlock, [2,2,2,2], bad_conditioning = True,num_classes=num_classes)


def ResNet34():
    return ResNet(BasicBlock, [3,4,6,3])

def ResNet50():
    return ResNet(Bottleneck, [3,4,6,3])

def ResNet101():
    return ResNet(Bottleneck, [3,4,23,3])

def ResNet152():
    return ResNet(Bottleneck, [3,8,36,3])

